# Strategies (/features/strategies)

Build pairs trades, rank a universe, or combine several strategies into one portfolio. These worked
examples bring VBT's data, indicators, signals, and analytics together on real market data, with
code and plots you can adapt to your own ideas.

## Choose a strategy workflow \[#choose-a-strategy-workflow]

| What do you want to research?                        | Where to start                                                                                                                                                       |
| ---------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Trade the spread between two related assets          | [Pairs trading](/features/strategies/pairs-trading/) covers pair selection, rolling hedge ratios, spread signals, and two-leg backtests.                             |
| Rank a universe and rotate into selected assets      | [Cross-sectional strategies](/features/strategies/cross-sectional-strategies/) covers momentum, custom factor scores, quintile portfolios, and changing eligibility. |
| Allocate between several trading strategies          | [Multi-strategy portfolios](/features/strategies/multi-strategy-portfolios/) compares static weights, scheduled rebalancing, and shared trading cash.                |
| Compare regular contributions with investing at once | [Recurring investing](/features/strategies/multi-strategy-portfolios/#recurring-investing-and-dollar-cost-averaging) backtests monthly deposits against a lump sum.  |

*   [Pairs trading](/features/strategies/pairs-trading): Screen cointegrated pairs, trade spread z-scores with shared cash, and validate them
*   [Cross-sectional strategies](/features/strategies/cross-sectional-strategies): Rank a universe each period, rotate into the leaders, and test factors by quintile
*   [Multi-strategy portfolios](/features/strategies/multi-strategy-portfolios): Stack strategies as one portfolio, weight them, and compare recurring investing plans

## Build on the examples \[#build-on-the-examples]

The examples use Python functions, pandas tables, and VBT portfolios, so you can replace their
symbols, scores, signals, and allocation rules with your own. Use
[signal backtesting](/features/backtesting/signal-backtesting/) when you have entry and exit arrays,
or [event-driven backtesting](/features/backtesting/event-driven-backtesting/) when a decision
depends on the positions and cash already in the portfolio.

The same research tools work across these strategy families. Compare lookbacks, thresholds, and
position sizes with [parameter optimization](/features/optimization/strategy-optimization/), then
check the selected settings on later data with
[walk-forward testing](/features/optimization/time-series-cross-validation/). Inspect
[trades](/features/analytics/trade-analytics/) and
[drawdowns](/features/analytics/drawdown-analysis/) to understand what drives the result.

Compare each idea with a baseline, inspect its trades, and see the effect of costs and allocation
rules. The examples give you a complete starting point for the next experiment.

## Start here \[#start-here]

Follow the [Pairs trading tutorial](/tutorials/pairs-trading/) to build a pairs strategy from
screening to a custom simulator. For a first signals-based example, the
[Basic RSI tutorial](/tutorials/basic-rsi/) goes from indicator values to a backtest. The
[Portfolio optimization tutorial](/tutorials/portfolio-optimization/) develops allocation functions
and connects them to portfolio simulation.